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Scan AI

How our AI Ecosystem can benefit your business

Why Use AI?

The best decisions made in any business or organisation are those that are supported by analysis of the data that the company has at its disposal. An average business only ever analyses around 10% of its data, whether this be sales figures, customer habits, demographic or geographic trends. This is usually because an entire organisation’s data is vast and the compute power and time to analyse more is too time consuming or costly; however the deeper you analyse the more insight you get and the better decisions, changes and improvements you can make.

The rise of GPUs, delivering hugely parallel compute power, and the growth in available data has led to the ability to analyse the majority or even all of an organisation’s data - but crucially in much shorter timeframes than ever before. The huge range of GPUs on the market now enable even a single workstation to add value by rapidly analysing datasets, and with larger budgets the sky's the limit when it comes to GPU-accelerated datacentres. This is why the use of AI and deep learning technologies is in exponential growth within almost every business sector.

Case Studies

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inooLabs

Using Natural Language Processing inooLabs has built a product which delivers information to students on any given topic from multiple sources in a simple manner.

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University of Liverpool

Using DGX A100 to map and understand molecules

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King’s College London

King’s College London is bringing artificial intelligence in medical imaging allowing the results gained from X-rays, CT or MRI scans to be delivered immediately at the time of the patient-doctor interaction.

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EnvirometriX and OpenGeoHub

Learn how OpenGeoHub and EnvirometriX pioneered a global predictive vegetation and soil mapping system using AI technology.

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FDL Europe 2020 - Clouds and Aerosols

The project team used observations from a geo-stationary satellite over the Southern Atlantic Ocean, combined with data from ECMWF and IMERG estimates to better understand the aerosol impact on cloud structure through the application of multiple machine learning methods.

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FDL Europe 2020 - Digital Twin Earth

The DTE project set out to discover whether machine learning can learn forecast precipitation by fusing simulated satellite weather data with physical model data, to offer a low-cost alternative to expensive simulation infrastructure.

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AI in Research & Higher Education

Higher education is at the frontlines of major global challenges, training innovators in AI, accelerated computing, and data science. At the same time, institutions need to meet the demand for more flexible, accessible education options. The ability to handle large workloads, increase efficiency, and lower operational costs with centralised infrastructure and computational excellence is key and this can be delivered in any learning environment by using GPU-accelerated AI and high-performance computing (HPC), to enable researchers to leverage modelling, simulation, and experimental datasets to address even the biggest challenges.

Research

Today’s research requires infrastructure that can handle large computational workloads to derive fast and accurate insights from vast amounts of data. Deep learning systems lowers the cost of computing infrastructure and accelerates the performance of HPC and AI applications.

AI Training

Get hands-on training in AI, data science, and accelerated computing to solve real-world problems. Through online courses and instructor-led workshops students can learn the latest techniques for designing and deploying neural networks.

Pre-Trained AI models

Building AI models can be complex and time-consuming but NVIDIA GPU systems are supported by a hub of essential software for deep learning, machine learning, and HPC with pre-trained AI models, model training scripts, and industry-specific software stacks.

AI for the Energy Industry

Energy touches every aspect of modern society, from our personal lives to impacting world economies. Today, the industry faces challenges in exploring new resources, reducing costs, maintaining safe conditions for workers and communities, and ensuring reliable energy delivery to customers. There are numerous ways that GPU-accelerated systems can help address these challenges including enhancing the performance of complex geophysical and engineering applications to reduce time to results. The high compute power, massively paralleled processors and high-speed memory of GPUs allow oil and gas companies to visualise and analyse petabytes of well location data in milliseconds, implement advanced algorithms to locate faults in underground structure and use deep learning training on raw seismic trace data to accelerate exploration and discover faults in geology.

Protective Health & Environment

Ensure that proper personal protective equipment (PPE) protocols are followed and safety hazards are identified by using AI to observe equipment, predict and detect failures, and save lives.

Predictive Maintenance

Avoid blackouts, downtimes, and unnecessary maintenance costs by identifying discrepancies in machinery in real time and predicting the remaining useful life of equipment. AI offers the visualisation and analysis of massive volumes of production and sensor data such as pump pressures, flow rates, and temperatures.

Inspection

AI models can be deployed to inspect and identify potential problems in hard to reach areas such as oil rig exteriors, high roofing or remote pipelines. Using drones instead of humans not only improves safety but increases accuracy and speed of fault finding.

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TurinTech

Learn how TurinTech enables businesses to automatically build accurate and explainable AI that runs faster. The answer is the Evolutionary Optimisation Platform.

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Cognitiv+

With Cognitiv+ Out of the Box Solution, it’s easy to analyse your documents, extract key provisions, save time and mitigates risks involved in document review.

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AI for the Finance Industry

Financial services business face a growing number of challenges - massive datasets, perpetual market fluctuations, the need for swift analysis and rising demand for personalised assistance. Intelligent GPU-accelerated technologies can address critical challenges within the modern financial services industry allowing institutions to boost risk management, make better data-backed decisions, increase security and reduce fraud - all whilst enhancing customer experiences with advances like natural language processing (NLP) being implemented to better tailor business - customer interactions.

Computational Risk

Accurate forecasts are critical for the performance of businesses. An AI platform accelerates the creation of models that help financial experts assess trends, identify risks, and ensure better information for prospective planning.

Accelerated Computing for Trading

Faster processing results in successful trade execution and increased revenue. GPU-powered hardware acceleration decreases latency, allowing operations to remain competitive.

Fraud Detection

The complexity of fraudulent activity, such as payment theft and money laundering, has evolved in proportionate to advancements in technology. Deep learning dramatically reduces false positives in transactional fraud.

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Peptone

Accelerating protein-based drug discovery using computational molecular physics

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Bering, iCAIRD and NHSGCC

Learn how AI-powered chest X-ray image diagnosis technology is being adapted to help early identification of COVID-19 cases.

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King’s College London

King’s College London is bringing artificial intelligence in medical imaging allowing the results gained from X-rays, CT or MRI scans to be delivered immediately at the time of the patient-doctor interaction.

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Vet AI

Joii, which was developed in accordance with world-class vets, enables pet owners to make the best choice for their dog or cat.

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TurinTech

Learn how TurinTech enables businesses to automatically build accurate and explainable AI that runs faster. The answer is the Evolutionary Optimisation Platform.

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AI for the Healthcare Industry

The world’s leading health organisations are equipping their doctors and scientists with GPU-accelerated compute systems to drive AI adoption - helping them transform lives and the future of research. With AI, they can tackle interoperable data, meet the increasing demand for personalised medicine and next-generation clinics, develop intelligent applications unique to their workflows, and accelerate areas such as image analysis, scientific research and drug discovery.

Virtual Reality

AI is being employed to develop robotic and virtual theatres so operations can be remotely performed and training given using virtual reality technology.

Imaging and Analytics

AI-driven medical image analysis is being used to increase accuracy and reduce diagnosis times, freeing consultants up to perform more surgeries.

Drug Creation

Deep learning in drug creation can massively speed up the process of discovery by running simulations and decoding data faster.

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TurinTech

Learn how TurinTech enables businesses to automatically build accurate and explainable AI that runs faster. The answer is the Evolutionary Optimisation Platform.

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AI for Manufacturing

The most progressive industrial companies in the world are implementing GPU-accelerated technologies to deploy large-scale AI initiatives. GPU-accelerated computing enables deep learning and AI at industrial scale, letting you take advantage of unprecedented amounts of sensor and operational data to bring down labour costs, reduce product defects, shorten unplanned downtimes, improve transition times, and increase production speed. AI -powered visual analytics is also being used to improve employ safety and compliance when using personal protection equipment (PPE).

Industrial Inspection

NVIDIA GPUs are used to develop the most accurate automated inspection solutions for manufacturing semiconductors, electronics, automotive components, and assemblies.

Predictive Maintenance

GPU-accelerated predictive maintenance solutions are helping industrial companies drive down operational costs by delivering greater accuracy than traditional machine learning-based methods in predicting equipment failure.

Robotics in Manufacturing

AI-enabled smart factories are changing the landscape of manufacturing. This includes everything from compact robots trained in specific tasks and autonomous rovers delivering parts in manufacturing plants to cooperative robots working together with people.

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Oxford Robotics Institute

Learning to understand objects in images without supervision for robotics applications.

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Oxford Robotics Institute

Next Steps: Learning a Disentangled Gait Representation for Versatile Quadruped Locomotion

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Oxford Robotics Institute

Leveraging Translational Invariance of the Fourier Transform for Efficient and Accurate Radar Odometry

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AI for the Robotics Industry

From smart automation in manufacturing to last-mile delivery, robots are becoming more ubiquitous in everyday life. Today's robots can do more than just perform tasks - they can learn, adapt, and evolve using capabilities such as machine learning, computer vision, navigation and more. The AI in robots not only helps to learn the model to perform certain tasks but also makes machines more intelligently react to different scenarios. (Please create an image like on the other tabs to illustrate robotics)

Healthcare

From medical supplies, to sanitisation, disinfection and performing remote surgeries, AI in robotics is making such machines become more intelligent from the large amount of patient data available.

Manufacturing

Except for a few important tasks, there are many processes performed by robotics to develop product such as cars, reducing the cost of manufacturing and removing the human workforce from performing such repetitive tasks.

Warehousing & Supply Chain

Robotics within logistics and supply chain, such as inventory handling at warehouses, plays a crucial role in moving the items transported by the logistic companies.

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Gaist

Using AI to understand the condition of highway infrastructure and assets and enable detailed lifecycle planning. Read more

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TurinTech

Learn how TurinTech enables businesses to automatically build accurate and explainable AI that runs faster. The answer is the Evolutionary Optimisation Platform.

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AI for the Transportation Industry

Autonomous vehicles will transform the way we live, work, and play, creating safer and more efficient roads. With the power of GPU-accelerated systems embedded within vehicles, AI models can be deployed to improve road safety, increase productivity and develop greener transportation. Embedded platforms can simultaneously process data from up a wide array of sensors, constantly collecting vital data for building a robust autonomous driving training library spanning a wide variety of traffic scenarios and conditions - essential for the development of safe self-driving vehicles.

Freedom of Mobility

More than 500 million hearing- or visually-impaired people and a growing ageing population worldwide must rely on others to travel every day.

Give Back Valuable Time

Commuters spend hundreds of hours behind the wheel each year—time that could be used to work, play, or spend time with family.

Improve Road Safety

Traffic crashes cause between 20 to 50 million injuries around the world each year, costing billions in damages and delays.

AI for the Surveillance Industry

Surveillance using multiple sensors at the edge connected to AI-powered analytics has the ability to influence outcomes in a number of environments - retail, warehousing, logistics and entertainment. Leading organisations are leveraging AI to reduce shrinkage, improve forecasting, automate warehouse logistics, determine in-store promotions and real-time pricing, deliver personalisation and recommendations to customers, and deliver better experiences both in stores, theatres, casinos and more.

Asset Protection

Retailers worldwide are losing billions per year to shrinkage, however the use of intelligent video analytics can accurately and efficiently reduce shrinkage.

Store Analytics

In-store data generated from point-of-sale transactions, cameras, and sensors are rife with insights that help determine customer preferences.

Ensuring Public Safety

Retail and entertainment spaces have had to rapidly shift to meet the needs of their customers—accommodating new purchasing behaviours, increased demand, and health safety needs.

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TurinTech

Learn how TurinTech enables businesses to automatically build accurate and explainable AI that runs faster. The answer is the Evolutionary Optimisation Platform.

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AI for the Smart Cities

AI, powered by GPU-accelerated computing, has the ability to deliver new ways to create more sustainable cities, maintain infrastructure, and improve public services for residents and communities. It all starts with the ability to gather data from trillions of sensors and other IoT devices and extract actionable insights in real time.

Faster Response Time

Process and analyse data in real time to improve operational efficiency, resource allocation, and disaster response.

Secure Deployment & Scalability

Deploy AI from the edge to the cloud with a range of appliances, from the NVIDIA Jetson embedded platform range able to handle an entire city’s sensors.

Traffic Management

Perform realtime traffic flow analysis and re-routing to ensure minimal commuter delays and optimal public transport performance.

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TurinTech

Learn how TurinTech enables businesses to automatically build accurate and explainable AI that runs faster. The answer is the Evolutionary Optimisation Platform.

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Interaction Care

Interaction Care uses a unique combination of technologies to help our clients proactively protect their customers and employees on site from high-risk situations that could jeopardise their health and safety.

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AI for the Retail Sector

Leading retailers are leveraging AI to reduce shrinkage, improve forecasting, automate warehouse logistics, determine in-store promotions and real-time pricing, enable customer personalisation and recommendations, and deliver better shopping experiences—both in stores and online. Understanding customer behaviour has never been more critical for retailers looking to drive growth. AI applications powered by video analytics can give retailers the same visibility into customer behaviour in stores as they currently have online.

Intelligent Stores

Using data from cameras and sensors, retailers are leveraging AI to reduce shrinkage, eliminate stockout, and gain visibility into customer behaviours. The same infrastructure can also power faster checkouts.

Forecasting & Inventory Management

AI is also improving demand forecasting and inventory management. Demand forecasting uses data from various sources to ensure the right products are available in the right store at the right time.

AI in Warehouse Logistics

Warehouse logistics is the art of optimising, integrating, automating, and managing the flow of products in fulfilment or distribution centres. Combining automation and management in supply chains leads to operational efficiency and higher process throughput.

The Scan AI Ecosystem Explained

The Scan AI ecosystem has been created so you have a trusted partner at every stage of your AI journey - whether you’re in early development of a model, in the midst of repeated training cycles or working on a complete inference deployment. Our in-house AI experts, backed by our system design and build teams are able to offer bespoke solutions that solve your AI challenges. Furthermore, we are complimented by a whole host of hardware and software partners enabling us to deliver end-to-end AI optimised architectures configured to get the maximum return from your investment and the deepest insight from your data.

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Guided proof of concept

Our proof of concept hardware environment ensures you choose the correct solution for your particular AI challenge. Housed within a number of secure data centres we operate a wide variety of GPU-accelerated AI systems. These range from development workstations, multi-GPU servers, NVIDIA DGX appliances, software-defined storage solutions and Advantech modular inferencing solutions. The ability to test a data set, check system compatibility or simply try-before-you-buy across such a varied degree of hardware and software solutions, we believe, is unique in the UK AI industry. Why not enquire today?

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GPU Accelerated Systems

Deep learning and AI relies hugely on parallel computing - only truly delivered by GPUs. Our status as the UK’s leading NVIDIA Elite Solution Provider enables us to offer a vast range of deep learning workstations and NVIDIA certified Data Science Workstations for AI development. We also can provide bespoke multi-GPU servers and NVIDIA DGX solutions too - all available with the latest NVIDIA GPUs. Lastly our range extends into NVIDIA Jetson based modular inferencing solutions - optimised for deployment in wide-reaching scenarios such as factories, transport and smart cities.

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Optimised Storage

Having the latest and greatest GPU accelerated training hardware is all well and good, but how do you ensure those powerful GPUs are being utilised as much as possible and always working at their hardest? AI optimised all-flash storage provides the data throughput required to get the most from your servers - a single storage appliance can deliver data to multiple multi-GPU servers, ensuring faster results and minimal downtime. Scan partners with PNY, NetApp, Dell, DDN and IBM to ensure we have an AI optimised storage solution that is right for you.

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Fast Connectivity

As your training infrastructure grows you need to ensure that bottlenecks aren’t introduced between your GPU-accelerated hardware and optimised storage systems. High bandwidth, low latency networks from NVIDIA Networking (formerly Mellanox) is the answer. Whether you’re looking to use Ethernet or Infiniband technologies, the wide range of interface cards, switches and cabling provides the best possible interconnects for your AI architecture - and also the fastest speeds of up to 400Gbps.

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Software Solutions

Many of the data science tasks required for AI model development can be time consuming and impede progress. For example prior to deep learning, any data must be verified, resized and labelled – it may also be required to transfer the data between programs to do this. Recognising this, Scan has teamed up with a number of partners to offer solutions that automate many of these processes, or deliver visual insight into what the data you have means. Adding software like these into your ecosystem not only reduces time to results, but optimises time spent by data scientists.

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Secure Managed Hosting

Although AI projects start small - maybe just a single deep learning workstation - they do scale, and quickly. As a project grows and hardware demand increases it is not always possible to have the space or security required to continue work on expanding datasets, especially if they are of the sensitive kind. With this in mind we’ve teamed up with several secure datacentre partners to offer hosted solutions - so you have the room to grow, but the peace of mind that the integrity of your data is never in doubt.

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Professional Services

As your own AI infrastructure grows we can help ensure you’re always getting the best from it. Whether its enhanced support during a proof of concept trial, system health checks, installation of new hardware, data science assistance or cyber security advice and solutions, the Scan AI team offers a whole host of consultancy services to support you through all your AI challenges.

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Education & Training

We’re never too old to stop learning, and in a technology as fast-paced as AI, continued education is essential to remain at the cutting edge. To address this requirement Scan has become a certified delivery partner for the NVIDIA Deep Learning Institute (DLI). This means that our AI experts are fully accredited to teach a host of topics around deep learning and AI. We offer a range of courses scheduled throughout the year so your teams can keep developing their knowledge, and new starters can learn the fundamentals of AI.

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Beginner’s Guide to Deep Learning & AI

Now you know how AI and deep learning could help your organisation, why not read on to learn more about the process of deep learning, its terminology, how to prepare your data and where to get started.

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